Our conditional FedGAN follows the recipe of DCGAN [1]}, one of the most widely used conditional GAN frameworks, as our basic network. We replace the generator architecture of DCGAN with that of StyleGAN2-ADA [2]}, given its generator produces images with high qualities in the majority of datasets and has great potential to generate high-resolution medical images for clinical research [3]}. It is worth noting that the attack and defense strategies although demonstrated with our selected architecture, they have the potential to apply to other GAN-based generative models. In all training scenarios, we adopt the Adam optimizer with a learning rate of \(2 \times 10^{-4}\)  for both the generator and the discriminator. The batch size is set to 32 as per the limit of a 32GB Tesla V100 GPU.
